평가 기준 개발하기
다양한 RAG 구성과 개선 사항을 체계적으로 검증하고 비교할 수 있는 사용자 지정 데이터셋과 기준을 만듭니다.
평가 기준 개발하기은(는) CoddyKit의 무료 LLM Apps in Production (RAG + Vector DB + Caching) 강의입니다. 이것은 4개 중 2번째 강의입니다. 아래에서 전체 강의를 무료로 읽을 수 있으며, 내장 코드 에디터와 24/7 AI 튜터와 함께 브라우저에서 직접 실습할 수 있습니다. 이 강의는 LLM Apps in Production (RAG + Vector DB + Caching) 학습 경로의 일부이며, 진행 상황이 웹과 CoddyKit 앱에 동기화됩니다. LLM Apps in Production (RAG + Vector DB + Caching) 강의에는 총 4개의 강의가 포함되어 있습니다.
이 강의의 일부는 아직 번역되지 않았으며 영어로 표시됩니다.
Why RAG Benchmarks Matter
Welcome! In this lesson, we'll learn how to create custom evaluation benchmarks for your RAG systems. Benchmarks are like custom test sets that help you measure how well your RAG application performs.
They are crucial for understanding improvements, regressions, and ensuring your RAG system delivers accurate and relevant information to your users.
Custom Benchmarks: The Why
While public datasets like SQuAD or HotpotQA are great for general LLM evaluation, they often don't reflect your specific use case or domain.
- Domain Specificity: Your RAG needs to answer questions about your data.
- Nuance & Complexity: Public datasets might not capture the unique challenges your users face.
- Continuous Improvement: Custom benchmarks allow you to track performance against your evolving needs.
What Makes a RAG Benchmark?
A robust RAG evaluation benchmark typically consists of a few key parts:
- Query Set: A collection of representative questions or prompts.
- Ground Truth: The "correct" answers or relevant documents for each query.
- Evaluation Metrics: The criteria you'll use to measure performance (e.g., accuracy, relevance).
We'll focus on the first two components in this lesson.
Building a Great Query Set
Your query set should mirror the types of questions real users will ask. Think about:
- Real User Data: Analyze actual user queries or common support tickets.
- Diverse Topics: Cover a wide range of subjects relevant to your RAG's knowledge base.
- Varying Difficulty: Include simple, complex, and even ambiguous questions.
- Edge Cases: Don't forget queries that might challenge your system.
Example: Query Generation
You can start by manually crafting queries or by using an LLM to generate them based on your documents. Here's a simple Python example of a query set structure:
queries = [
"What are the benefits of cloud computing?",
"Explain the capital gains tax in detail.",
"How do I reset my account password?",
"What is the company's policy on remote work?",
"List common cybersecurity threats."
]
for q in queries:
print(f"Query: {q}")Establishing Ground Truth
Ground truth is the gold standard against which your RAG's output is measured. For RAG, this often means identifying:
- Relevant Documents: Which specific documents should be retrieved for a given query?
- Correct Answers: What is the ideal answer based on those documents?
This step often requires human expertise to ensure accuracy.
Structuring Ground Truth
Ground truth can be stored in a structured way, linking queries to their expected relevant context and answers. This allows for automated evaluation.
ground_truth = {
"What are the benefits of cloud computing?": {
"relevant_docs": ["doc_cloud_intro.txt", "doc_cloud_benefits.pdf"],
"answer": "Scalability, cost savings, flexibility, and reliability."
},
"How do I reset my account password?": {
"relevant_docs": ["doc_password_reset_guide.html"],
"answer": "Go to settings, click 'Forgot Password', and follow the prompts."
}
}
for query, gt in ground_truth.items():
print(f"Query: {query}")
print(f" Expected Docs: {gt['relevant_docs']}")
print(f" Expected Answer: {gt['answer']}\n")The Human Touch: Annotation
Creating high-quality ground truth often involves human annotation. This means:
- Experts Review: Subject matter experts identify relevant documents and craft ideal answers.
- Crowdsourcing: For larger datasets, platforms can be used, but quality control is vital.
- Consistency: Clear guidelines are essential to ensure annotators label data uniformly.
This ensures your benchmark accurately reflects "correctness."
Benchmarks Evolve
Your RAG system and its data will change over time, and so should your benchmarks! Treat your evaluation benchmarks as living assets:
- Add New Queries: Incorporate new user questions or emerging topics.
- Update Ground Truth: As your knowledge base grows, update expected answers.
- Retire Old Data: Remove outdated information that is no longer relevant.
Regular review keeps your benchmark effective.
Benchmark Essentials
Which of the following are essential components of a robust RAG evaluation benchmark?
Recap: Building Benchmarks
Great job! You've learned how to develop custom evaluation benchmarks for your RAG system. We covered:
- The importance of custom, domain-specific benchmarks.
- The core components: query sets and ground truth.
- Strategies for crafting representative queries and defining accurate ground truth.
- The role of human annotation and iterative refinement.
Next, we'll explore how to use these benchmarks to apply key metrics for RAG performance evaluation!
AI 튜터와 함께 LLM Apps in Production (RAG + Vector DB + Caching)을(를) 배우세요 — 무료
브라우저에서 실제 코드를 작성하고 실행하며, 24/7 AI 튜터로부터 즉각적인 도움을 받고, 웹이나 앱에서 중단한 부분부터 계속 학습하세요.
- 코스
- 12
- 레슨
- 48
자주 묻는 질문
“평가 기준 개발하기” 강의는 무료인가요?
네 — “평가 기준 개발하기” 전체 내용을 이 웹사이트에서 무료로 읽을 수 있습니다. 인터랙티브하게 실습하려면(내장 코드 에디터와 24/7 AI 튜터), CoddyKit PRO로 업그레이드하면 LLM Apps in Production (RAG + Vector DB + Caching) 강의 전체를 잠금 해제할 수 있습니다. LLM Apps in Production (RAG + Vector DB + Caching) 강의에는 총 4개의 강의가 포함되어 있습니다.
“평가 기준 개발하기”에서 뭘 배우나요?
다양한 RAG 구성과 개선 사항을 체계적으로 검증하고 비교할 수 있는 사용자 지정 데이터셋과 기준을 만듭니다. 브라우저에서 직접 실행하는 실습 코드로 LLM Apps in Production (RAG + Vector DB + Caching)을(를) 배우며, 24/7 AI 튜터가 강의를 진행하면서 질문에 답변해줍니다.
LLM Apps in Production (RAG + Vector DB + Caching)을(를) 시작하는 데 경험이 필요한가요?
사전 경험은 필요하지 않습니다. CoddyKit의 LLM Apps in Production (RAG + Vector DB + Caching)은(는) 초급자부터 고급 학습자까지를 위해 구성되어 있으므로, 여기서 시작하거나 처음부터 시작할 수 있으며 자신의 속도대로 진행할 수 있습니다. 이것은 4개 중 2번째 강의입니다.
“평가 기준 개발하기” 강의는 얼마나 걸리나요?
대부분의 CoddyKit 강의는 약 5~10분이 소요됩니다. 각 강의는 간결하고 인터랙티브하여 꾸준한 진행이 가능하며, 웹과 앱에서 중단한 부분부터 바로 시작할 수 있습니다.
이 LLM Apps in Production (RAG + Vector DB + Caching) 강의에서 코드를 작성하고 실행할 수 있나요?
네. 모든 LLM Apps in Production (RAG + Vector DB + Caching) 강의에는 내장 코드 에디터가 포함되어 있으므로, 브라우저에서 바로 실제 코드를 작성하고 실행한 후 즉시 AI 피드백을 받을 수 있습니다 — 로컬 설정이 필요 없습니다.